



"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase							:       Tuple          =       logging.get_logger(__name__)

__lowerCAmelCase							:       Any          =       {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''conditional_detr'''
		_lowerCamelCase      	=							['''past_key_values''']
		_lowerCamelCase      	=							{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}





		def __init__(       self				,     _lowercase=True				,     _lowercase=None				,     _lowercase=3				,     _lowercase=3_0_0				,     _lowercase=6				,     _lowercase=2_0_4_8				,     _lowercase=8				,     _lowercase=6				,     _lowercase=2_0_4_8				,     _lowercase=8				,     _lowercase=0.0				,     _lowercase=0.0				,     _lowercase=True				,     _lowercase="relu"				,     _lowercase=2_5_6				,     _lowercase=0.1				,     _lowercase=0.0				,     _lowercase=0.0				,     _lowercase=0.02				,     _lowercase=1.0				,     _lowercase=False				,     _lowercase="sine"				,     _lowercase="resnet50"				,     _lowercase=True				,     _lowercase=False				,     _lowercase=2				,     _lowercase=5				,     _lowercase=2				,     _lowercase=1				,     _lowercase=1				,     _lowercase=2				,     _lowercase=5				,     _lowercase=2				,     _lowercase=0.25				,     **_lowercase				,     )			->    Tuple:







								'''simple docstring'''

								if backbone_config is not None and use_timm_backbone:
														raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )

								if not use_timm_backbone:
														if backbone_config is None:
																				logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
																				snake_case_ :			Dict            =      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
														elif isinstance(_lowercase				,     _lowercase ):
																				snake_case_ :			List[str]            =      backbone_config.get("""model_type""" )
																				snake_case_ :			Tuple            =      CONFIG_MAPPING[backbone_model_type]
																				snake_case_ :			Any            =      config_class.from_dict(_lowercase )

								snake_case_ :			Optional[int]            =      use_timm_backbone
								snake_case_ :			Dict            =      backbone_config
								snake_case_ :			List[str]            =      num_channels
								snake_case_ :			List[str]            =      num_queries
								snake_case_ :			List[Any]            =      d_model
								snake_case_ :			List[Any]            =      encoder_ffn_dim
								snake_case_ :			Union[str, Any]            =      encoder_layers
								snake_case_ :			List[str]            =      encoder_attention_heads
								snake_case_ :			str            =      decoder_ffn_dim
								snake_case_ :			Tuple            =      decoder_layers
								snake_case_ :			int            =      decoder_attention_heads
								snake_case_ :			str            =      dropout
								snake_case_ :			List[Any]            =      attention_dropout
								snake_case_ :			List[Any]            =      activation_dropout
								snake_case_ :			List[str]            =      activation_function
								snake_case_ :			Union[str, Any]            =      init_std
								snake_case_ :			Union[str, Any]            =      init_xavier_std
								snake_case_ :			int            =      encoder_layerdrop
								snake_case_ :			Union[str, Any]            =      decoder_layerdrop
								snake_case_ :			List[Any]            =      encoder_layers
								snake_case_ :			Tuple            =      auxiliary_loss
								snake_case_ :			Union[str, Any]            =      position_embedding_type
								snake_case_ :			Optional[int]            =      backbone
								snake_case_ :			Any            =      use_pretrained_backbone
								snake_case_ :			Optional[int]            =      dilation
								# Hungarian matcher
								snake_case_ :			int            =      class_cost
								snake_case_ :			Optional[int]            =      bbox_cost
								snake_case_ :			str            =      giou_cost
								# Loss coefficients
								snake_case_ :			str            =      mask_loss_coefficient
								snake_case_ :			Optional[int]            =      dice_loss_coefficient
								snake_case_ :			int            =      cls_loss_coefficient
								snake_case_ :			Any            =      bbox_loss_coefficient
								snake_case_ :			Any            =      giou_loss_coefficient
								snake_case_ :			int            =      focal_alpha
								super().__init__(is_encoder_decoder=_lowercase				,     **_lowercase )





		@property
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								return self.encoder_attention_heads





		@property
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								return self.d_model





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_ :			List[str]            =      copy.deepcopy(self.__dict__ )
								if self.backbone_config is not None:
														snake_case_ :			Tuple            =      self.backbone_config.to_dict()
								snake_case_ :			Union[str, Any]            =      self.__class__.model_type
								return output




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							version.parse('''1.11'''		)





		@property
		def  UpperCAmelCase__				(       self )			->    Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								return OrderedDict(
								    [
								        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
								        ("""pixel_mask""", {0: """batch"""}),
								    ] )





		@property
		def  UpperCAmelCase__				(       self )			->    float:







								'''simple docstring'''

								return 1E-5





		@property
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								return 1_2


"""simple docstring"""


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       **__lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =AutoConfig.from_pretrained(__lowerCAmelCase  ,       **__lowerCAmelCase							)
						lowerCamelCase__   =AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase							)
						model.save_pretrained(__lowerCAmelCase							)
						AutoTokenizer.from_pretrained(__lowerCAmelCase							).save_pretrained(__lowerCAmelCase							)
						return model


if __name__ == "__main__":
	fire.Fire(save_randomly_initialized_version)


